Source code for colour.appearance.hunt

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
Hunt Colour Appearance Model
============================

Defines Hunt colour appearance model objects:

-   :class:`Hunt_InductionFactors`
-   :attr:`HUNT_VIEWING_CONDITIONS`
-   :class:`Hunt_Specification`
-   :func:`XYZ_to_Hunt`

See Also
--------
`Hunt Colour Appearance Model IPython Notebook
<http://nbviewer.ipython.org/github/colour-science/colour-ipython/blob/master/notebooks/appearance/hunt.ipynb>`_  # noqa

References
----------
.. [1]  Fairchild, M. D. (2013). The Hunt Model. In Color Appearance Models
        (3rd ed., pp. 5094–5556). Wiley. ASIN:B00DAYO8E2
.. [2]  Hunt, R. W. G. (2004). The Reproduction of Colour (6th ed.). Wiley.
        ISBN:978-0-470-02425-6
"""

from __future__ import division, unicode_literals

import numpy as np
from collections import namedtuple

from colour.utilities import (
    CaseInsensitiveMapping,
    dot_vector,
    tsplit,
    tstack,
    warning)

__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013 - 2015 - Colour Developers'
__license__ = 'GPL V3.0 - http://www.gnu.org/licenses/'
__maintainer__ = 'Colour Developers'
__email__ = 'colour-science@googlegroups.com'
__status__ = 'Production'

__all__ = ['Hunt_InductionFactors',
           'HUNT_VIEWING_CONDITIONS',
           'HUE_DATA_FOR_HUE_QUADRATURE',
           'XYZ_TO_HPE_MATRIX',
           'HPE_TO_XYZ_MATRIX',
           'Hunt_ReferenceSpecification',
           'Hunt_Specification',
           'XYZ_to_Hunt',
           'luminance_level_adaptation_factor',
           'illuminant_scotopic_luminance',
           'XYZ_to_rgb',
           'f_n',
           'chromatic_adaptation',
           'adjusted_reference_white_signals',
           'achromatic_post_adaptation_signal',
           'colour_difference_signals',
           'hue_angle',
           'eccentricity_factor',
           'low_luminance_tritanopia_factor',
           'yellowness_blueness_response',
           'redness_greenness_response',
           'overall_chromatic_response',
           'saturation_correlate',
           'achromatic_signal',
           'brightness_correlate',
           'lightness_correlate',
           'chroma_correlate',
           'colourfulness_correlate']


[docs]class Hunt_InductionFactors( namedtuple('Hunt_InductionFactors', ('N_c', 'N_b', 'N_cb', 'N_bb'))): """ Hunt colour appearance model induction factors. Parameters ---------- N_c : numeric or array_like Chromatic surround induction factor :math:`N_c`. N_b : numeric or array_like *Brightness* surround induction factor :math:`N_b`. N_cb : numeric or array_like, optional Chromatic background induction factor :math:`N_{cb}`, approximated using tristimulus values :math:`Y_w` and :math:`Y_b` of respectively the reference white and the background if not specified. N_bb : numeric or array_like, optional *Brightness* background induction factor :math:`N_{bb}`, approximated using tristimulus values :math:`Y_w` and :math:`Y_b` of respectively the reference white and the background if not specified. """ def __new__(cls, N_c, N_b, N_cb=None, N_bb=None): """ Returns a new instance of the :class:`Hunt_InductionFactors` class. """ return super(Hunt_InductionFactors, cls).__new__( cls, N_c, N_b, N_cb, N_bb)
HUNT_VIEWING_CONDITIONS = CaseInsensitiveMapping( {'Small Areas, Uniform Background & Surrounds': ( Hunt_InductionFactors(1, 300)), 'Normal Scenes': ( Hunt_InductionFactors(1, 75)), 'Television & CRT, Dim Surrounds': ( Hunt_InductionFactors(1, 25)), 'Large Transparencies On Light Boxes': ( Hunt_InductionFactors(0.7, 25)), 'Projected Transparencies, Dark Surrounds': ( Hunt_InductionFactors(0.7, 10))}) """ Reference Hunt colour appearance model viewing conditions. HUNT_VIEWING_CONDITIONS : CaseInsensitiveMapping **{'Small Areas, Uniform Background & Surrounds', 'Normal Scenes', 'Television & CRT, Dim Surrounds', 'Large Transparencies On Light Boxes', 'Projected Transparencies, Dark Surrounds'}** Aliases: - 'small_uniform': 'Small Areas, Uniform Background & Surrounds' - 'normal': 'Normal Scenes' - 'tv_dim': 'Television & CRT, Dim Surrounds' - 'light_boxes': 'Large Transparencies On Light Boxes' - 'projected_dark': 'Projected Transparencies, Dark Surrounds' """ HUNT_VIEWING_CONDITIONS['small_uniform'] = ( HUNT_VIEWING_CONDITIONS['Small Areas, Uniform Background & Surrounds']) HUNT_VIEWING_CONDITIONS['normal'] = ( HUNT_VIEWING_CONDITIONS['Normal Scenes']) HUNT_VIEWING_CONDITIONS['tv_dim'] = ( HUNT_VIEWING_CONDITIONS['Television & CRT, Dim Surrounds']) HUNT_VIEWING_CONDITIONS['light_boxes'] = ( HUNT_VIEWING_CONDITIONS['Large Transparencies On Light Boxes']) HUNT_VIEWING_CONDITIONS['projected_dark'] = ( HUNT_VIEWING_CONDITIONS['Projected Transparencies, Dark Surrounds']) HUE_DATA_FOR_HUE_QUADRATURE = { 'h_s': np.array([20.14, 90.00, 164.25, 237.53]), 'e_s': np.array([0.8, 0.7, 1.0, 1.2])} XYZ_TO_HPE_MATRIX = np.array( [[0.38971, 0.68898, -0.07868], [-0.22981, 1.18340, 0.04641], [0.00000, 0.00000, 1.00000]]) """ Hunt colour appearance model *CIE XYZ* tristimulus values to *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace matrix. XYZ_TO_HPE_MATRIX : array_like, (3, 3) """ HPE_TO_XYZ_MATRIX = np.linalg.inv(XYZ_TO_HPE_MATRIX) """ Hunt colour appearance model *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace to *CIE XYZ* tristimulus values matrix. HPE_TO_XYZ_MATRIX : array_like, (3, 3) """
[docs]class Hunt_ReferenceSpecification( namedtuple('Hunt_ReferenceSpecification', ('J', 'C_94', 'h_S', 's', 'Q', 'M_94', 'H', 'H_C'))): """ Defines the Hunt colour appearance model reference specification. This specification has field names consistent with Fairchild (2013) reference. Parameters ---------- J : numeric or array_like Correlate of *Lightness* :math:`J`. C_94 : numeric or array_like Correlate of *chroma* :math:`C_94`. h_S : numeric or array_like *Hue* angle :math:`h_S` in degrees. s : numeric or array_like Correlate of *saturation* :math:`s`. Q : numeric or array_like Correlate of *brightness* :math:`Q`. M_94 : numeric or array_like Correlate of *colourfulness* :math:`M_94`. H : numeric or array_like *Hue* :math:`h` quadrature :math:`H`. H_C : numeric or array_like *Hue* :math:`h` composition :math:`H_C`. """
[docs]class Hunt_Specification( namedtuple('Hunt_Specification', ('J', 'C', 'h', 's', 'Q', 'M', 'H', 'HC'))): """ Defines the Hunt colour appearance model specification. This specification has field names consistent with the remaining colour appearance models in :mod:`colour.appearance` but diverge from Fairchild (2013) reference. Parameters ---------- J : numeric or array_like Correlate of *Lightness* :math:`J`. C : numeric or array_like Correlate of *chroma* :math:`C_94`. h : numeric or array_like *Hue* angle :math:`h_S` in degrees. s : numeric or array_like Correlate of *saturation* :math:`s`. Q : numeric or array_like Correlate of *brightness* :math:`Q`. M : numeric or array_like Correlate of *colourfulness* :math:`M_94`. H : numeric or array_like *Hue* :math:`h` quadrature :math:`H`. HC : numeric or array_like *Hue* :math:`h` composition :math:`H_C`. """
[docs]def XYZ_to_Hunt(XYZ, XYZ_w, XYZ_b, L_A, surround=HUNT_VIEWING_CONDITIONS.get('Normal Scenes'), L_AS=None, CCT_w=None, XYZ_p=None, p=None, S=None, S_W=None, helson_judd_effect=False, discount_illuminant=True): """ Computes the Hunt colour appearance model correlates. Parameters ---------- XYZ : array_like *CIE XYZ* tristimulus values of test sample / stimulus in domain [0, 100]. XYZ_w : array_like *CIE XYZ* tristimulus values of reference white in domain [0, 100]. XYZ_b : array_like *CIE XYZ* tristimulus values of background in domain [0, 100]. L_A : numeric or array_like Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`. surround : Hunt_InductionFactors, optional Surround viewing conditions induction factors. L_AS : numeric or array_like, optional Scotopic luminance :math:`L_{AS}` of the illuminant, approximated if not specified. CCT_w : numeric or array_like, optional Correlated color temperature :math:`T_{cp}`: of the illuminant, needed to approximate :math:`L_{AS}`. XYZ_p : array_like, optional *CIE XYZ* tristimulus values of proximal field in domain [0, 100], assumed to be equal to background if not specified. p : numeric or array_like, optional Simultaneous contrast / assimilation factor :math:`p` with value in domain [-1, 0] when simultaneous contrast occurs and domain [0, 1] when assimilation occurs. S : numeric or array_like, optional Scotopic response :math:`S` to the stimulus, approximated using tristimulus values :math:`Y` of the stimulus if not specified. S_w : numeric or array_like, optional Scotopic response :math:`S_w` for the reference white, approximated using the tristimulus values :math:`Y_w` of the reference white if not specified. helson_judd_effect : bool, optional Truth value indicating whether the *Helson-Judd* effect should be accounted for. discount_illuminant : bool, optional Truth value indicating if the illuminant should be discounted. Warning ------- The input domain of that definition is non standard! Notes ----- - Input *CIE XYZ* tristimulus values are in domain [0, 100]. - Input *CIE XYZ_b* tristimulus values are in domain [0, 100]. - Input *CIE XYZ_w* tristimulus values are in domain [0, 100]. - Input *CIE XYZ_p* tristimulus values are in domain [0, 100]. Returns ------- Hunt_Specification Hunt colour appearance model specification. Raises ------ ValueError If an illegal arguments combination is specified. Examples -------- >>> XYZ = np.array([19.01, 20.00, 21.78]) >>> XYZ_w = np.array([95.05, 100.00, 108.88]) >>> XYZ_b = np.array([95.05, 100.00, 108.88]) >>> L_A = 318.31 >>> surround = HUNT_VIEWING_CONDITIONS['Normal Scenes'] >>> CCT_w = 6504.0 >>> XYZ_to_Hunt( # doctest: +ELLIPSIS ... XYZ, XYZ_w, XYZ_b, L_A, surround, CCT_w=CCT_w) Hunt_Specification(J=30.0462678..., C=0.1210508..., h=269.2737594..., \ s=0.0199093..., Q=22.2097654..., M=0.1238964..., H=None, HC=None) """ _X, Y, Z = tsplit(XYZ) X_b, Y_b, _Z_b = tsplit(XYZ_b) _X_w, Y_w, Z_w = tsplit(XYZ_w) # Arguments handling. if XYZ_p is not None: X_p, Y_p, Z_p = tsplit(XYZ_p) else: X_p = X_b Y_p = Y_b Z_p = Y_b warning('Unspecified proximal field "XYZ_p" argument, using ' 'background "XYZ_b" as approximation!') if surround.N_cb is None: N_cb = 0.725 * (Y_w / Y_b) ** 0.2 warning('Unspecified "N_cb" argument, using approximation: ' '"{0}"'.format(N_cb)) if surround.N_bb is None: N_bb = 0.725 * (Y_w / Y_b) ** 0.2 warning('Unspecified "N_bb" argument, using approximation: ' '"{0}"'.format(N_bb)) if L_AS is None and CCT_w is None: raise ValueError('Either the scotopic luminance "L_AS" of the ' 'illuminant or its correlated colour temperature ' '"CCT_w" must be specified!') if L_AS is None: L_AS = illuminant_scotopic_luminance(L_A, CCT_w) warning('Unspecified "L_AS" argument, using approximation from "CCT": ' '"{0}"'.format(L_AS)) if S is None != S_W is None: raise ValueError('Either both stimulus scotopic response "S" and ' 'reference white scotopic response "S_w" arguments ' 'need to be specified or none of them!') elif S is None and S_W is None: S = Y S_W = Y_w warning('Unspecified stimulus scotopic response "S" and reference ' 'white scotopic response "S_w" arguments, using ' 'approximation: "{0}", "{1}"'.format(S, S_W)) if p is None: warning('Unspecified simultaneous contrast / assimilation "p" ' 'argument, model will not account for simultaneous chromatic ' 'contrast!') XYZ_p = tstack((X_p, Y_p, Z_p)) # Computing luminance level adaptation factor :math:`F_L`. F_L = luminance_level_adaptation_factor(L_A) # Computing test sample chromatic adaptation. rgb_a = chromatic_adaptation(XYZ, XYZ_w, XYZ_b, L_A, F_L, XYZ_p, p, helson_judd_effect, discount_illuminant) # Computing reference white chromatic adaptation. rgb_aw = chromatic_adaptation(XYZ_w, XYZ_w, XYZ_b, L_A, F_L, XYZ_p, p, helson_judd_effect, discount_illuminant) # Computing opponent colour dimensions. # Computing achromatic post adaptation signals. A_a = achromatic_post_adaptation_signal(rgb_a) A_aw = achromatic_post_adaptation_signal(rgb_aw) # Computing colour difference signals. C = colour_difference_signals(rgb_a) C_w = colour_difference_signals(rgb_aw) # ------------------------------------------------------------------------- # Computing the *hue* angle :math:`h_s`. # ------------------------------------------------------------------------- h = hue_angle(C) # hue_w = hue_angle(C_w) # TODO: Implement hue quadrature & composition computation. # ------------------------------------------------------------------------- # Computing the correlate of *saturation* :math:`s`. # ------------------------------------------------------------------------- # Computing eccentricity factors. e_s = eccentricity_factor(h) # Computing low luminance tritanopia factor :math:`F_t`. F_t = low_luminance_tritanopia_factor(L_A) M_yb = yellowness_blueness_response(C, e_s, surround.N_c, N_cb, F_t) M_rg = redness_greenness_response(C, e_s, surround.N_c, N_cb) M_yb_w = yellowness_blueness_response(C_w, e_s, surround.N_c, N_cb, F_t) M_rg_w = redness_greenness_response(C_w, e_s, surround.N_c, N_cb) # Computing overall chromatic response. M = overall_chromatic_response(M_yb, M_rg) M_w = overall_chromatic_response(M_yb_w, M_rg_w) s = saturation_correlate(M, rgb_a) # ------------------------------------------------------------------------- # Computing the correlate of *brightness* :math:`Q`. # ------------------------------------------------------------------------- # Computing achromatic signal :math:`A`. A = achromatic_signal(L_AS, S, S_W, N_bb, A_a) A_w = achromatic_signal(L_AS, S_W, S_W, N_bb, A_aw) Q = brightness_correlate(A, A_w, M, surround.N_b) brightness_w = brightness_correlate(A_w, A_w, M_w, surround.N_b) # TODO: Implement whiteness-blackness :math:`Q_{wb}` computation. # ------------------------------------------------------------------------- # Computing the correlate of *Lightness* :math:`J`. # ------------------------------------------------------------------------- J = lightness_correlate(Y_b, Y_w, Q, brightness_w) # ------------------------------------------------------------------------- # Computing the correlate of *chroma* :math:`C_{94}`. # ------------------------------------------------------------------------- C_94 = chroma_correlate(s, Y_b, Y_w, Q, brightness_w) # ------------------------------------------------------------------------- # Computing the correlate of *colourfulness* :math:`M_{94}`. # ------------------------------------------------------------------------- M_94 = colourfulness_correlate(F_L, C_94) return Hunt_Specification(J, C_94, h, s, Q, M_94, None, None)
[docs]def luminance_level_adaptation_factor(L_A): """ Returns the *luminance* level adaptation factor :math:`F_L`. Parameters ---------- L_A : numeric or array_like Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`. Returns ------- numeric or ndarray *Luminance* level adaptation factor :math:`F_L` Examples -------- >>> luminance_level_adaptation_factor(318.31) # doctest: +ELLIPSIS 1.1675444... """ L_A = np.asarray(L_A) k = 1 / (5 * L_A + 1) k4 = k ** 4 F_L = 0.2 * k4 * (5 * L_A) + 0.1 * (1 - k4) ** 2 * (5 * L_A) ** (1 / 3) return F_L
[docs]def illuminant_scotopic_luminance(L_A, CCT): """ Returns the approximate scotopic luminance :math:`L_{AS}` of the illuminant. Parameters ---------- L_A : numeric or array_like Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`. CCT : numeric or array_like Correlated color temperature :math:`T_{cp}` of the illuminant. Returns ------- numeric or ndarray Approximate scotopic luminance :math:`L_{AS}`. Examples -------- >>> illuminant_scotopic_luminance(318.31, 6504.0) # doctest: +ELLIPSIS 769.9376286... """ L_A = np.asarray(L_A) CCT = np.asarray(CCT) CCT = 2.26 * L_A * ((CCT / 4000) - 0.4) ** (1 / 3) return CCT
[docs]def XYZ_to_rgb(XYZ): """ Converts from *CIE XYZ* tristimulus values to *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace. Parameters ---------- XYZ : array_like *CIE XYZ* tristimulus values. Returns ------- ndarray *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace. Examples -------- >>> XYZ = np.array([19.01, 20.00, 21.78]) >>> XYZ_to_rgb(XYZ) # doctest: +ELLIPSIS array([ 19.4743367..., 20.3101217..., 21.78 ]) """ return dot_vector(XYZ_TO_HPE_MATRIX, XYZ)
[docs]def f_n(x): """ Defines the nonlinear response function of the Hunt colour appearance model used to model the nonlinear behavior of various visual responses. Parameters ---------- x : numeric or array_like or array_like Visual response variable :math:`x`. Returns ------- numeric or array_like Modeled visual response variable :math:`x`. Examples -------- >>> x = np.array([0.23350512, 0.23351103, 0.23355179]) >>> f_n(x) # doctest: +ELLIPSIS array([ 5.8968592..., 5.8969521..., 5.8975927...]) """ x = np.asarray(x) x_m = 40 * ((x ** 0.73) / (x ** 0.73 + 2)) return x_m
[docs]def chromatic_adaptation(XYZ, XYZ_w, XYZ_b, L_A, F_L, XYZ_p=None, p=None, helson_judd_effect=False, discount_illuminant=True): """ Applies chromatic adaptation to given *CIE XYZ* tristimulus values. Parameters ---------- XYZ : array_like *CIE XYZ* tristimulus values of test sample in domain [0, 100]. XYZ_b : array_like *CIE XYZ* tristimulus values of background in domain [0, 100]. XYZ_w : array_like *CIE XYZ* tristimulus values of reference white in domain [0, 100]. L_A : numeric or array_like Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`. F_L : numeric or array_like Luminance adaptation factor :math:`F_L`. XYZ_p : array_like, optional *CIE XYZ* tristimulus values of proximal field in domain [0, 100], assumed to be equal to background if not specified. p : numeric or array_like, optional Simultaneous contrast / assimilation factor :math:`p` with value in domain [-1, 0] when simultaneous contrast occurs and domain [0, 1] when assimilation occurs. helson_judd_effect : bool, optional Truth value indicating whether the *Helson-Judd* effect should be accounted for. discount_illuminant : bool, optional Truth value indicating if the illuminant should be discounted. Returns ------- ndarray Adapted *CIE XYZ* tristimulus values. Examples -------- >>> XYZ = np.array([19.01, 20.00, 21.78]) >>> XYZ_b = np.array([95.05, 100.00, 108.88]) >>> XYZ_w = np.array([95.05, 100.00, 108.88]) >>> L_A = 318.31 >>> F_L = 1.16754446415 >>> chromatic_adaptation(XYZ, XYZ_w, XYZ_b, L_A, F_L) # doctest: +ELLIPSIS array([ 6.8959454..., 6.8959991..., 6.8965708...]) """ XYZ_w = np.asarray(XYZ_w) XYZ_b = np.asarray(XYZ_b) L_A = np.asarray(L_A) F_L = np.asarray(F_L) rgb = XYZ_to_rgb(XYZ) rgb_w = XYZ_to_rgb(XYZ_w) Y_w = XYZ_w[..., 1] Y_b = XYZ_b[..., 1] h_rgb = 3 * rgb_w / np.sum(rgb_w, axis=-1)[..., np.newaxis] # Computing chromatic adaptation factors. if not discount_illuminant: F_rgb = ((1 + (L_A ** (1 / 3)) + h_rgb) / (1 + (L_A ** (1 / 3)) + (1 / h_rgb))) else: F_rgb = np.ones(h_rgb.shape) # Computing Helson-Judd effect parameters. if helson_judd_effect: D_rgb = (f_n((Y_b / Y_w) * F_L * F_rgb[..., 1]) - f_n((Y_b / Y_w) * F_L * F_rgb)) else: D_rgb = np.zeros(F_rgb.shape) # Computing cone bleach factors. B_rgb = (10 ** 7) / ((10 ** 7) + 5 * L_A[..., np.newaxis] * (rgb_w / 100)) # Computing adjusted reference white signals. if XYZ_p is not None and p is not None: rgb_p = XYZ_to_rgb(XYZ_p) rgb_w = adjusted_reference_white_signals(rgb_p, B_rgb, rgb_w, p) # Computing adapted cone responses. rgb_a = 1 rgb_a += B_rgb * (f_n(F_L[..., np.newaxis] * F_rgb * rgb / rgb_w) + D_rgb) return rgb_a
[docs]def adjusted_reference_white_signals(rgb_p, rgb_b, rgb_w, p): """ Adjusts the white point for simultaneous chromatic contrast. Parameters ---------- rgb_p : array_like Cone signals *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array of the proximal field. rgb_b : array_like Cone signals *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array of the background. rgb_w : array_like Cone signals array *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array of the reference white. p : numeric or array_like Simultaneous contrast / assimilation factor :math:`p` with value in domain [-1, 0] when simultaneous contrast occurs and domain [0, 1] when assimilation occurs. Returns ------- ndarray Adjusted cone signals *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array of the reference white. Examples -------- >>> rgb_p = np.array([98.07193550, 101.13755950, 100.00000000]) >>> rgb_b = np.array([0.99984505, 0.99983840, 0.99982674]) >>> rgb_w = np.array([97.37325710, 101.54968030, 108.88000000]) >>> p = 0.1 >>> adjusted_reference_white_signals( # doctest: +ELLIPSIS ... rgb_p, rgb_b, rgb_w, p) array([ 88.0792742..., 91.8569553..., 98.4876543...]) """ rgb_p = np.asarray(rgb_p) rgb_b = np.asarray(rgb_b) rgb_w = np.asarray(rgb_w) p = np.asarray(p) p_rgb = rgb_p / rgb_b rgb_w = (rgb_w * (((1 - p) * p_rgb + (1 + p) / p_rgb) ** 0.5) / (((1 + p) * p_rgb + (1 - p) / p_rgb) ** 0.5)) return rgb_w
[docs]def achromatic_post_adaptation_signal(rgb): """ Returns the achromatic post adaptation signal :math:`A` from given *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array. Parameters ---------- rgb : array_like *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array. Returns ------- numeric or ndarray Achromatic post adaptation signal :math:`A`. Examples -------- >>> rgb = np.array([6.89594549, 6.89599915, 6.89657085]) >>> achromatic_post_adaptation_signal(rgb) # doctest: +ELLIPSIS 18.9827186... """ r, g, b = tsplit(rgb) A = 2 * r + g + (1 / 20) * b - 3.05 + 1 return A
[docs]def colour_difference_signals(rgb): """ Returns the colour difference signals :math:`C_1`, :math:`C_2` and :math:`C_3` from given *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array. Parameters ---------- rgb : array_like *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array. Returns ------- ndarray Colour difference signals :math:`C_1`, :math:`C_2` and :math:`C_3`. Examples -------- >>> rgb = np.array([6.89594549, 6.89599915, 6.89657085]) >>> colour_difference_signals(rgb) # doctest: +ELLIPSIS array([ -5.3660000...e-05, -5.7170000...e-04, 6.2536000...e-04]) """ r, g, b = tsplit(rgb) C_1 = r - g C_2 = g - b C_3 = b - r C = tstack((C_1, C_2, C_3)) return C
[docs]def hue_angle(C): """ Returns the *hue* angle :math:`h` from given colour difference signals :math:`C`. Parameters ---------- C : array_like Colour difference signals :math:`C`. Returns ------- numeric or ndarray *Hue* angle :math:`h`. Examples -------- >>> C = np.array([-5.3658655819965873e-05, ... -0.00057169938364687312, ... 0.00062535803946683899]) >>> hue_angle(C) # doctest: +ELLIPSIS 269.2737594... """ C_1, C_2, C_3 = tsplit(C) hue = (180 * np.arctan2(0.5 * (C_2 - C_3) / 4.5, C_1 - (C_2 / 11)) / np.pi) % 360 return hue
[docs]def eccentricity_factor(hue): """ Returns eccentricity factor :math:`e_s` from given hue angle :math:`h`. Parameters ---------- hue : numeric or array_like Hue angle :math:`h`. Returns ------- numeric or ndarray Eccentricity factor :math:`e_s`. Examples -------- >>> eccentricity_factor(269.273759) # doctest: +ELLIPSIS array(1.1108365...) """ hue = np.asarray(hue) h_s = HUE_DATA_FOR_HUE_QUADRATURE.get('h_s') e_s = HUE_DATA_FOR_HUE_QUADRATURE.get('e_s') x = np.interp(hue, h_s, e_s) x = np.where(hue < 20.14, 0.856 - (hue / 20.14) * 0.056, x) x = np.where(hue > 237.53, 0.856 + 0.344 * (360 - hue) / (360 - 237.53), x) return x
[docs]def low_luminance_tritanopia_factor(L_A): """ Returns the low luminance tritanopia factor :math:`F_t` from given adapting field *luminance* :math:`L_A` in :math:`cd/m^2`. Parameters ---------- L_A : numeric or array_like Adapting field *luminance* :math:`L_A` in :math:`cd/m^2`. Returns ------- numeric or ndarray Low luminance tritanopia factor :math:`F_t`. Examples -------- >>> low_luminance_tritanopia_factor(318.31) # doctest: +ELLIPSIS 0.9996859... """ L_A = np.asarray(L_A) F_t = L_A / (L_A + 0.1) return F_t
[docs]def yellowness_blueness_response(C, e_s, N_c, N_cb, F_t): """ Returns the yellowness / blueness response :math:`M_{yb}`. Parameters ---------- C : array_like Colour difference signals :math:`C`. e_s : numeric or array_like Eccentricity factor :math:`e_s`. N_c : numeric or array_like Chromatic surround induction factor :math:`N_c`. N_b : numeric or array_like Brightness surround induction factor :math:`N_b`. F_t : numeric or array_like Low luminance tritanopia factor :math:`F_t`. Returns ------- numeric or ndarray Yellowness / blueness response :math:`M_{yb}`. Examples -------- >>> C = np.array([-5.3658655819965873e-05, ... -0.00057169938364687312, ... 0.00062535803946683899]) >>> e_s = 1.1108365048626296 >>> N_c = 1.0 >>> N_cb = 0.72499999999999998 >>> F_t = 0.99968593951195 >>> yellowness_blueness_response( # doctest: +ELLIPSIS ... C, e_s, N_c, N_cb, F_t) -0.0082372... """ _C_1, C_2, C_3 = tsplit(C) e_s = np.asarray(e_s) N_c = np.asarray(N_c) N_cb = np.asarray(N_cb) F_t = np.asarray(F_t) M_yb = (100 * (0.5 * (C_2 - C_3) / 4.5) * (e_s * (10 / 13) * N_c * N_cb * F_t)) return M_yb
[docs]def redness_greenness_response(C, e_s, N_c, N_cb): """ Returns the redness / greenness response :math:`M_{yb}`. Parameters ---------- C : array_like Colour difference signals :math:`C`. e_s : numeric or array_like Eccentricity factor :math:`e_s`. N_c : numeric or array_like Chromatic surround induction factor :math:`N_c`. N_b : numeric or array_like Brightness surround induction factor :math:`N_b`. Returns ------- numeric or ndarray Redness / greenness response :math:`M_{rg}`. Examples -------- >>> C = np.array([-5.3658655819965873e-05, ... -0.00057169938364687312, ... 0.00062535803946683899]) >>> e_s = 1.1108365048626296 >>> N_c = 1.0 >>> N_cb = 0.72499999999999998 >>> redness_greenness_response(C, e_s, N_c, N_cb) # doctest: +ELLIPSIS -0.0001044... """ C_1, C_2, _C_3 = tsplit(C) e_s = np.asarray(e_s) N_c = np.asarray(N_c) N_cb = np.asarray(N_cb) M_rg = 100 * (C_1 - (C_2 / 11)) * (e_s * (10 / 13) * N_c * N_cb) return M_rg
[docs]def overall_chromatic_response(M_yb, M_rg): """ Returns the overall chromatic response :math:`M`. Parameters ---------- M_yb : numeric or array_like Yellowness / blueness response :math:`M_{yb}`. M_rg : numeric or array_like Redness / greenness response :math:`M_{rg}`. Returns ------- numeric or ndarray Overall chromatic response :math:`M`. Examples -------- >>> M_yb = -0.008237223618824608 >>> M_rg = -0.00010444758327626432 >>> overall_chromatic_response(M_yb, M_rg) # doctest: +ELLIPSIS 0.0082378... """ M_yb = np.asarray(M_yb) M_rg = np.asarray(M_rg) M = ((M_yb ** 2) + (M_rg ** 2)) ** 0.5 return M
[docs]def saturation_correlate(M, rgb_a): """ Returns the *saturation* correlate :math:`s`. Parameters ---------- M : numeric or array_like Overall chromatic response :math:`M`. rgb_a : array_like Adapted *Hunt-Pointer-Estevez* :math:`\\rho\gamma\\beta` colourspace array. Returns ------- numeric or ndarray *Saturation* correlate :math:`s`. Examples -------- >>> M = 0.008237885787274198 >>> rgb_a = np.array([6.89594549, 6.89599915, 6.89657085]) >>> saturation_correlate(M, rgb_a) # doctest: +ELLIPSIS 0.0199093... """ M = np.asarray(M) rgb_a = np.asarray(rgb_a) s = 50 * M / np.sum(rgb_a, axis=-1) return s
[docs]def achromatic_signal(L_AS, S, S_W, N_bb, A_a): """ Returns the achromatic signal :math:`A`. Parameters ---------- L_AS : numeric or array_like Scotopic luminance :math:`L_{AS}` of the illuminant. S : numeric or array_like Scotopic response :math:`S` to the stimulus. S_w : numeric or array_like Scotopic response :math:`S_w` for the reference white. N_bb : numeric or array_like Brightness background induction factor :math:`N_{bb}`. A_a: numeric or array_like Achromatic post adaptation signal of the stimulus :math:`A_a`. Returns ------- numeric or ndarray Achromatic signal :math:`A`. Examples -------- >>> L_AS = 769.9376286541402 >>> S = 20.0 >>> S_W = 100.0 >>> N_bb = 0.72499999999999998 >>> A_a = 18.982718664838487 >>> achromatic_signal(L_AS, S, S_W, N_bb, A_a) # doctest: +ELLIPSIS 15.5068546... """ L_AS = np.asarray(L_AS) S = np.asarray(S) S_W = np.asarray(S_W) N_bb = np.asarray(N_bb) A_a = np.asarray(A_a) j = 0.00001 / ((5 * L_AS / 2.26) + 0.00001) # Computing scotopic luminance level adaptation factor :math:`F_{LS}`. F_LS = 3800 * (j ** 2) * (5 * L_AS / 2.26) F_LS += 0.2 * ((1 - (j ** 2)) ** 0.4) * ((5 * L_AS / 2.26) ** (1 / 6)) # Computing cone bleach factors :math:`B_S`. B_S = 0.5 / (1 + 0.3 * ((5 * L_AS / 2.26) * (S / S_W)) ** 0.3) B_S += 0.5 / (1 + 5 * (5 * L_AS / 2.26)) # Computing adapted scotopic signal :math:`A_S`. A_S = (f_n(F_LS * S / S_W) * 3.05 * B_S) + 0.3 # Computing achromatic signal :math:`A`. A = N_bb * (A_a - 1 + A_S - 0.3 + np.sqrt((1 + (0.3 ** 2)))) return A
[docs]def brightness_correlate(A, A_w, M, N_b): """ Returns the *brightness* correlate :math:`Q`. Parameters ---------- A : numeric or array_like Achromatic signal :math:`A`. A_a: numeric or array_like Achromatic post adaptation signal of the reference white :math:`A_w`. M : numeric or array_like Overall chromatic response :math:`M`. N_b : numeric or array_like Brightness surround induction factor :math:`N_b`. Returns ------- numeric or ndarray *Brightness* correlate :math:`Q`. Examples -------- >>> A = 15.506854623621885 >>> A_w = 35.718916676317086 >>> M = 0.0082378857872741976 >>> N_b = 75.0 >>> brightness_correlate(A, A_w, M, N_b) # doctest: +ELLIPSIS 22.2097654... """ A = np.asarray(A) A_w = np.asarray(A_w) M = np.asarray(M) N_b = np.asarray(N_b) N_1 = ((7 * A_w) ** 0.5) / (5.33 * N_b ** 0.13) N_2 = (7 * A_w * N_b ** 0.362) / 200 Q = ((7 * (A + (M / 100))) ** 0.6) * N_1 - N_2 return Q
[docs]def lightness_correlate(Y_b, Y_w, Q, Q_w): """ Returns the *Lightness* correlate :math:`J`. Parameters ---------- Y_b : numeric or array_like Tristimulus values :math:`Y_b` the background. Y_w : numeric or array_like Tristimulus values :math:`Y_b` the reference white. Q : numeric or array_like *Brightness* correlate :math:`Q` of the stimulus. Q_w : numeric or array_like *Brightness* correlate :math:`Q` of the reference white. Returns ------- numeric or ndarray *Lightness* correlate :math:`J`. Examples -------- >>> Y_b = 100.0 >>> Y_w = 100.0 >>> Q = 22.209765491265024 >>> Q_w = 40.518065821226081 >>> lightness_correlate(Y_b, Y_w, Q, Q_w) # doctest: +ELLIPSIS 30.0462678... """ Y_b = np.asarray(Y_b) Y_w = np.asarray(Y_w) Q = np.asarray(Q) Q_w = np.asarray(Q_w) Z = 1 + (Y_b / Y_w) ** 0.5 J = 100 * (Q / Q_w) ** Z return J
[docs]def chroma_correlate(s, Y_b, Y_w, Q, Q_w): """ Returns the *chroma* correlate :math:`C_94`. Parameters ---------- s : numeric or array_like *Saturation* correlate :math:`s`. Y_b : numeric or array_like Tristimulus values :math:`Y_b` the background. Y_w : numeric or array_like Tristimulus values :math:`Y_b` the reference white. Q : numeric or array_like *Brightness* correlate :math:`Q` of the stimulus. Q_w : numeric or array_like *Brightness* correlate :math:`Q` of the reference white. Returns ------- numeric or ndarray *Chroma* correlate :math:`C_94`. Examples -------- >>> s = 0.0199093206929 >>> Y_b = 100.0 >>> Y_w = 100.0 >>> Q = 22.209765491265024 >>> Q_w = 40.518065821226081 >>> chroma_correlate(s, Y_b, Y_w, Q, Q_w) # doctest: +ELLIPSIS 0.1210508... """ s = np.asarray(s) Y_b = np.asarray(Y_b) Y_w = np.asarray(Y_w) Q = np.asarray(Q) Q_w = np.asarray(Q_w) C_94 = (2.44 * (s ** 0.69) * ((Q / Q_w) ** (Y_b / Y_w)) * (1.64 - 0.29 ** (Y_b / Y_w))) return C_94
[docs]def colourfulness_correlate(F_L, C_94): """ Returns the *colourfulness* correlate :math:`M_94`. Parameters ---------- F_L : numeric or array_like Luminance adaptation factor :math:`F_L`. numeric *Chroma* correlate :math:`C_94`. Returns ------- numeric *Colourfulness* correlate :math:`M_94`. Examples -------- >>> F_L = 1.16754446414718 >>> C_94 = 0.12105083993617581 >>> colourfulness_correlate(F_L, C_94) # doctest: +ELLIPSIS 0.1238964... """ F_L = np.asarray(F_L) C_94 = np.asarray(C_94) M_94 = F_L ** 0.15 * C_94 return M_94